BESS Predictive Control for High Dynamic Load Balancing
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Solution Overview
Problem
Power distribution grids face challenges in managing high dynamic loads and renewable energy sources, leading to oversizing of components and reduced battery lifetime due to unpredictable power fluctuations and intermittent operations, which are economically and technically inefficient.
Innovation Solution
A method for controlling battery energy storage systems that predicts power generation and load demand using historical data and artificial intelligence, optimizing the state of charge and discharge of batteries to balance power supply and extend battery life, while supporting high dynamic loads and renewable sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If battery energy storage system is used to supply power during high demand periods, then power supply reliability is improved, but battery lifespan deteriorates due to excessive charging and discharging cycles
Solution Approach 1:
The system performs preliminary action by predicting future power generation from renewable sources and load demands using historical data and AI algorithms. This allows the battery management system to proactively plan charging and discharging cycles, avoiding unnecessary operations that would reduce battery lifespan while ensuring power supply reliability is maintained during critical periods.
Solution Approach 2:
The system implements dynamic control by continuously adjusting battery operation based on real-time conditions and predictions. The AI algorithm dynamically determines optimal charging and discharging schedules, adapting to changing renewable energy availability and load patterns, thereby minimizing unnecessary cycles while maintaining reliable power supply when needed.
2Power
If power system components are oversized to handle peak loads, then power supply capacity is improved, but installation cost increases
Solution Approach 1:
The system uses AI-based prediction of power generation and load patterns to perform preliminary planning of battery operation. This allows the power system to be sized appropriately for average conditions rather than peak demands, reducing installation costs while the predictive control ensures sufficient capacity is available during peak periods through optimized battery discharge.
Solution Approach 2:
The system changes operational parameters dynamically by adjusting battery charge/discharge rates and timing based on predicted conditions. This allows the power system to effectively provide peak power capacity without requiring permanently oversized components, as the battery can be charged during low-demand periods and discharged during peak periods when needed.
3Quantity of substance
If battery operates at extreme charge levels to maximize energy storage, then energy capacity is improved, but battery lifespan deteriorates
Solution Approach 1:
The system performs preliminary prediction of renewable energy generation and load patterns to determine optimal battery charge levels in advance. This allows the battery to operate within safe charge ranges that maximize energy storage while avoiding extreme charge/discharge cycles that would reduce lifespan, as the AI algorithm plans operations based on forecasted conditions.
Solution Approach 2:
The system implements feedback control by continuously monitoring battery state of charge and comparing it with optimal ranges determined by the AI algorithm. The control system adjusts charging and discharging operations based on this feedback to maintain battery operation within ranges that maximize both energy storage capacity and lifespan, preventing operation at harmful extreme levels.
Data Source
AI summary
A method of controlling battery energy storage comprises a renewable active power source (PR), a battery energy storage system (BESS), at least one static load (PLoad) and at least one high dynamic load PH.LOAD. Active power demand of the system in the point of common coupling is at quasi-constant level. The method further includes gathering constant active power limit from the external grid (PGRID) and historical data of active power profiles of: predicting the following active power profiles for day+1, calculating required active power of the battery energy storage system, setting daily peak of state of charge and minimum state of charge of the battery energy storage system, and verifying whether daily peak of state of charge and minimum state of charge ensure that instantaneous values of state of charge through the entire day of the battery energy storage system is within range of 20% to 80%.